VP AI Data Engineering, Business Insurance

The Hartford•Hartford, CT

6d•Hybrid

About The Position

The Hartford is seeking a Vice President of AI Data Engineering to lead the Business Insurance Data Engineering portfolio. The role shapes strategy, organizational design, and delivery of trusted, governed, scalable, and AI-ready data capabilities. It translates business strategy into data products, modernization priorities, AI-ready data foundations, workforce capabilities, and measurable outcomes in partnership with business and technology leaders. The position supports analytics, automation, machine learning, generative AI, retrieval-augmented generation (RAG), and agentic AI across Business Insurance. The role leads the Business Insurance agentic analytics strategy and builds data-intelligence capabilities that enable AI and analytics to interpret enterprise data in its business context. It establishes reusable semantic layers and governed metrics across underwriting, pricing, distribution, servicing, claims, risk, customer, operations, and finance, while defining controls and operating practices for agentic data and analytics. The Vice President leads a large, complex organization through senior leaders and owns portfolio delivery, operational performance, cost effectiveness, workforce outcomes, risk management, control maturity, audit readiness, and value realization. The role partners with business and technology executives, governance bodies, strategic partners, and vendors to align priorities and deliver Business Insurance outcomes. The position may be based in Hartford, Connecticut, or the NYC/NJ metro area, with a hybrid work arrangement. Applicants must be eligible to work in the United States without company sponsorship.

Requirements

Bachelor's degree in Computer Science, Data Engineering, Information Systems, Engineering, Applied Mathematics, Business, or a related field.
18 or more years of applicable experience in Data Engineering, data platforms, AI-ready data ecosystems, software engineering for data-intensive systems, technology leadership, or related disciplines.
10 or more years of progressive leadership experience, including leadership of senior leaders, large technical organizations, complex portfolios, or business-critical enterprise capabilities.
Demonstrated success translating business strategy into Data Engineering strategy, operating models, portfolio priorities, resource plans, workforce capabilities, and multi-year roadmaps.
Proven experience establishing enterprise data-product, semantic-layer, or data-intelligence capabilities that improve trusted reuse, consistent business interpretation, and measurable outcomes.
Demonstrated leadership enabling advanced analytics, generative AI, RAG, or agentic analytics through governed, context-rich, and traceable enterprise data foundations.
Experience partnering across Data Engineering, Analytics, AI, Architecture, Product, Governance, Risk, and business teams to deliver shared capabilities and resolve complex ownership and operating-model decisions.
Proven experience managing significant budgets, workforce resources, strategic vendors, modernization priorities, and portfolio performance.
Strong business and financial acumen, with the ability to connect Data and AI capabilities to measurable Business Insurance outcomes.
Meaningful experience advising executive leaders, influencing strategic decisions, navigating complex tradeoffs, and leading organizational change, including adoption of AI-enabled ways of working.
Exceptional executive communication, judgment, negotiation, relationship management, conflict resolution, presentation, and storytelling skills.
Mastery of modern Data Engineering and AI-ready data foundations, including data products, pipelines, data contracts, semantic and contextual data, metadata, lineage, quality, observability, reliability, testing, and lifecycle management.
Deep expertise in enterprise data-intelligence patterns combining semantic layers, governed metrics, business glossaries, contextual metadata, ontologies, knowledge graphs, policies, lineage, and access controls.
Strong understanding of agentic analytics architectures and operating patterns, including tool and data access, agent identity and authorization, retrieval and grounding, orchestration, memory and context, human-in-the-loop controls, evaluation, observability, auditability, exception handling, and lifecycle management.
Deep understanding of data foundations for generative AI, RAG, and agentic AI, including embeddings, vector and graph technologies, retrieval structures, grounding data, trusted access, evidence traceability, and lifecycle governance.
Demonstrated ability to design reusable semantic models and governed business metrics that support consistent analysis and AI interpretation across Business Insurance functions.
Strong understanding of enterprise data architecture and platform concepts, and ability to partner effectively with Architecture and Platform Engineering while maintaining accountability for Data Engineering strategy and delivery.
Strong fluency in Python, SQL, distributed processing, Snowflake, Spark, Kafka, cloud-native technologies, orchestration, streaming, APIs, and hybrid or multi-cloud environments.
Experience designing and operating complex data ecosystems across cloud platforms such as AWS or Google Cloud, including large-scale data integration, warehouse, lake, lakehouse, domain, API, vector, and graph capabilities.
Strong understanding of AI-assisted engineering and the governance, human oversight, validation, traceability, workforce, and operational practices required for responsible adoption at scale.
Deep understanding of Data Governance, Responsible AI, privacy, security, model and data risk, auditability, regulatory obligations, compliance, records management, and enterprise control frameworks.
Demonstrated ability to evaluate technical, modernization, vendor, resource, operational, and risk tradeoffs using business, financial, technical, and regulatory context.
Must be eligible to work in the United States without company sponsorship.

Nice To Haves

Advanced degree preferred.
Significant Property & Casualty or Business Insurance experience strongly preferred.

Responsibilities

Define and champion the Business Insurance AI Data Engineering strategy and operating model, translating business priorities into multi-year portfolio plans, capability roadmaps, workforce requirements, and measurable outcomes.
Set priorities and allocate resources across data products, modernization, AI-ready data foundations, technical debt, operational resilience, and emerging business needs.
Ensure delivery and ongoing operation of trusted, reusable, scalable data products, pipelines, semantic assets, and data foundations for analytics, automation, machine learning, generative AI, RAG, and agentic AI.
Lead the Business Insurance agentic analytics strategy, enabling governed AI agents and decision-support experiences while maintaining human accountability for material business decisions.
Build and scale enterprise data-intelligence layers combining business semantics, contextual metadata, ontologies, knowledge graphs, metrics, policies, lineage, and access controls.
Establish reusable semantic layers and governed business metrics across underwriting, pricing, distribution, servicing, claims, risk, customer, operations, and finance.
Partner with Analytics, AI, Architecture, Product, Governance, and business leaders to deliver context-rich data products and retrieval patterns grounded in authoritative data, approved definitions, and traceable evidence.
Define operating models and controls for agentic data and analytics, including agent access, identity, authorization, observability, evaluation, audit trails, feedback loops, exception handling, and lifecycle management.
Connect Data Engineering capabilities to Business Insurance underwriting, pricing, distribution, servicing, claims, risk management, customer, operational, and financial outcomes.
Guide the evolution of structured and unstructured data capabilities, including metadata, semantic and contextual assets, embeddings, vector and graph technologies, retrieval and grounding data, and governed external data sources.
Establish expectations for data quality, testing, lineage, traceability, observability, reliability, lifecycle management, operational readiness, data contracts, service levels, and consumer accountability.
Champion responsible use of AI-assisted engineering to improve productivity, quality, testing, documentation, and delivery speed while maintaining human oversight and controls.
Lead a large, complex organization through senior leaders, strengthening leadership effectiveness, organizational health, succession, talent development, and long-term capability sustainability.
Own portfolio delivery, operational performance, cost effectiveness, workforce outcomes, risk management, control maturity, audit readiness, and value realization.
Partner with business and technical executives to align priorities, architecture direction, platform capabilities, governance requirements, dependencies, and outcomes while maintaining Data Engineering accountability.
Remove organizational and cross-functional barriers, guide major multidimensional initiatives, and adjust priorities using business, financial, workforce, operational, quality, and risk insights.
Represent Business Insurance AI Data Engineering with senior leaders, governance bodies, strategic partners, and vendors, while monitoring emerging data and AI trends and their business implications.